1. Field of the Invention
The present invention relates generally to a distributed analysis system and to a method that may be implemented for analyzing materials employing the system. More particularly, the system and method may relate to the optical analysis of materials to determine characteristics thereof.
2. Description of the Related Art
Analysis instruments, such as optical (e.g., Near Infra Red (NIR)) spectrophotometers, may be employed in a variety of industries to analyze various material samples to make a quantitative and/or a qualitative determination of various characteristics thereof, such as concentrations of constituents of the material and/or physical properties, for example. In agricultural and food industries, for example, the oil, protein and moisture content of grain and other crops; the fat content of meat; the fat, protein, lactose and urea content of milk; the quality of grain and of wine and other vinicultural products; may be determined using NIR analysis. It is also known to apply similar NIR analysis in the medical, pharmaceutical, chemical and petro-chemical industries.
An optical analysis of samples may be considered as an “indirect” determination because the optical measurements may be an indirect indication of the characteristic being determined. Results may be obtained in a matter of minutes (for example) in contrast to the conventional “direct”, sometimes chemical, analysis methods which may take hours or days (for example) to perform and which may be carried out at central laboratories that may be remote from the site where the sample was taken.
Since the optical analysis is an indirect determination, a link may be established between the optical measurement and the characteristic and/or property of interest (hereinafter referred to as “trait”). As is known in the art, the trait may be represented in an equation summing products of weighting coefficients and values from the derivative of the optical absorbance and/or transmission spectrum that may be acquired by the analysis instrument. A first order derivative of an absorbance spectrum may be used but higher order derivatives may also or alternatively be used. The undifferentiated absorbance (or in some cases transmission) spectrum and any or all derivatives thereof will be referred to jointly and/or severally as the context demands, as a “sample spectrum” or more generally as “sample data”.
To measure the desired trait of an unknown sample, spectra may be collected from a multiplicity of known sample materials similar to the unknown sample. The trait(s), be it a physical characteristic and/or a constituent concentration, to be determined may be known in the known samples. Using the collected sample spectra obtained from the known samples and from the knowledge of the associated trait, the weighting coefficients of the equations relating the known trait to the collected spectra may be determined by (for example) multiple regression, by partial least squares regression and/or by other statistical techniques including those employing artificial neural nets. The process of determining the values of the weighting coefficients may be known as “calibration”. After the calibration coefficients have been determined, the unknown sample may be analysed using the analysis instrument (and/or an instrument intended to be substantially identical in performance so that the same calibration coefficients may be applied) together with the calibration coefficient that have been derived from the known sample materials. Instead of measuring the spectral response at selected specific wavelengths, which may be known and/or may be presumed to correlate with the trait, the sample spectra may be collected at wavelengths distributed throughout the spectral region appropriate to the trait (e.g., the NIR spectral region) and coefficients and equations relating the trait to spectral measurements throughout that spectral region may be developed.
Calibration coefficients may be derived for each trait to be determined and for each type of sample material. Calibration coefficients may be collected in “calibration libraries” for access and use by a data processor which may be programmed to carry out the determination of the desired trait. Calibration libraries may additionally or alternatively include the complete calibration equations, including the calibration coefficients, for use by the data processor. The term “calibration model” may refer either jointly or severally to the calibration coefficients and the calibration equations associated with a particular trait, as demanded by the context in which it is employed herein. It will be understood that similar methodology may be applied to other types of sample data that may be generated using other indirect measurement modalities employed by other types of analysis instrument.
An analysis system which employs an NIR spectrophotometer is known from for example, U.S. Pat. No. 6,751,576 to Hall et al. and from US 2003/0122080 to Burling-Claridge et al. (the contents of both of which are incorporated herein by reference in their entirety). In both disclosures, the data processor may be located at a site that is remote of the analysis instrument and a communication link may be provided to permit data transfer between the analysis instrument and the data processor.
This known centralized analysis system may include a storage device that may be located at a site remote of the analysis instrument. For example, the storage device may be collocated with the data processor. The storage device may retain a calibration library for access by and use in the data processor. The data processor may select an appropriate calibration model from the library in dependence on data received from a particular remote analysis instrument over the communications link. The data processor may then apply the selected model to sample data which may be provided over the communications link. Using the same communications link, the trait determined as a result of the application of the selected calibration model may be provided to the local site, for example to an output device in the vicinity of the analysis instrument that generated the sample data. In this manner, the library of calibration models may be maintained centrally which may facilitate calibration model upgrading, for example as new known samples are added and/or as statistical analysis methodology is developed. Moreover, development of a calibration library may be costly and time consuming, and therefore maintaining the library centrally and remote from the user of an analysis instrument may provide greater control over the access to and use of the calibration models in the library.
Although conventional systems and methods are generally thought to provide acceptable performance, they are not without shortcomings. For example, sample data such as an optical spectrum, even if compressed, may represent a relatively large amount of data that may be transmitted over the communications link. Accordingly, measures may be taken to ensure that the communications link remains stable over the time period required to transmit the data and to ensure that the transmitted data is accurately represented at the central processor. Furthermore, the analysis results to be transmitted over the often publicly accessible communications link (e.g., a telecommunications link) may be sensitive information which, if misappropriated, may be used to the commercial disadvantage of the intended recipient.